ASI-GCN

Limited borehole sampling at contaminated sites results in sparse and unevenly distributed data on soil pollutants. Traditional interpolation methods, such as ordinary kriging (OK) and inverse distance weighting (IDW), may obscure local variations in soil contamination when applied to such sparse data, thus reducing the interpolation accuracy. To overcome this challenge, we propose an adaptive spatial interpolation graph convolutional network (ASI-GCN) model. This model synergistically integrated the principles of spatial auto-correlation with the capabilities of graph convolutional networks (GCN). By merging these elements, the ASI-GCN model effectively constrained the transfer of pollutant concentrations. It adeptly captures nuanced variations in spatial structure, thereby enhancing the precision of soil pollution characterization. We applied our model to a coking plant in Beijing based on 215 soil samples collected from 15 boreholes. To evaluate the robustness of the model, three pollutants with distinct volatilization characteristics were employed, including arsenic (As, non-volatile), benzo(a)pyrene (BaP, semi-volatile), and benzene (Ben, volatile). The leave-one-out cross-validation procedure was used to estimate the performance of ASI-GCN and its derivatives, ASI-GCN_RC_G and ASI-GCN_RC_K, when they are used to make predictions at unsampled sites.The results showed that the ASI-GCN model demonstrated improved performance with R² values of 0.52, 0.67, and 0.57, and RMSE values of 5.041 mg/kg, 1.699 mg/kg , and 167.710 mg/kg for As, BaP, and Ben, respectively, outperforming traditional models like OK and IDW. Further analysis revealed the significant influence of pollutant volatility on vertical migration patterns. Non-volatile As was primarily distributed in the fill and silty sand layers, semi-volatile BaP concentrated in the silty sand layer, while volatile Ben was predominantly found in the clay and fine sand layers. The ASI-GCN model provides key insights for risk assessment and remediation strategies by capturing complex contaminant distributions with limited borehole data, demonstrating its potential for application in various contaminated sites.